Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Journal of Applied Science, Technology & Humanities
Hanifah Khusnul Syahida
IPB University
Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Social Sciences

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Web Application Using Fuzzy Logic to Assess Recommended Intake Frequency of Extruded Chiki Snacks Bayu Praditya; Wuliddah Tamsil Barokah; Annisa Raihanah Maimun; Mrr Lukie Trianawati; Rinunti Najwa Putri Minanti; Nazwa Salsabila; Tulus Fernando Sihombing; Fairuz Aliyya Khansa; Aurellia Avisa Putri Alya; Rubina Alleysha Putri Aulia; Hanifah Khusnul Syahida; Roma Juliana Arios
Journal of Applied Science, Technology & Humanities | JASTH Vol. 2 No. 5 (2025): November 2025
Publisher : Batrisya Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62535/nt94t493

Abstract

Excessive consumption of extruded snacks, particularly Chiki products high in sugar, salt, and fat, increases the risk of obesity, hypertension, and metabolic disorders. This study aims to develop a web-based fuzzy logic application using the Mamdani inference system to determine the recommended frequency of consumption based on nutrient composition. The research employed a quantitative descriptive approach integrating three main input variables: sugar, salt, and fat, each represented by fuzzy sets of low, medium, and high levels. The output variable, consumption frequency, was classified into three linguistic terms: daily safe, moderate, and limited. A total of 27 fuzzy rules were constructed and simulated using MATLAB. The results showed that the model effectively translated quantitative nutritional data into qualitative recommendations, with higher nutrient concentrations corresponding to lower consumption frequency. The fuzzy Mamdani model provided smooth decision boundaries, demonstrating high interpretability and potential as a nutritional decision support system for consumer health guidance.
Co-Authors Annisa Raihanah Maimun Aurellia Avisa Putri Alya Bayu Praditya Fairuz Aliyya Khansa Mrr Lukie Trianawati Nazwa Salsabila Rinunti Najwa Putri Minanti Roma Juliana Arios Rubina Alleysha Putri Aulia Tulus Fernando Sihombing Wuliddah Tamsil Barokah